def build_apprentissage(fn,generator):
ex_raw = KeyboardStrategy.read(fn)
exemples = []
labels = []
for x in ex_raw:
exemples.append(generator(x[1],x[0][0],x[0][1]))
labels.append(x[0][2])
return exemples,labels
def build_apprentissage(fn, generator):
ex_raw = KeyboardStrategy.read(fn)
exemples = []
labels = []
for x in ex_raw:
exemples.append(generator(x[1], x[0][0], x[0][1]))
labels.append(x[0][2])
return exemples, labels
long = 10
sep1="|"+"-"*(long-1)
sepl="|"+" "*(long-1)
sepr=" "*long
def aux(node,sep):
if tree.tree_.children_left[node]<0:
ls ="(%s)" % (", ".join( "%s: %d" %(tree.classes_[i],int(x)) for i,x in enumerate(tree.tree_.value[node].flat)))
return sep+sep1+"%s\n" % (ls,)
return (sep+sep1+"X%d<=%0.2f\n"+"%s"+sep+sep1+"X%d>%0.2f\n"+"%s" )% \
(tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_left[node],sep+sepl),
tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_right[node],sep+sepr))
return aux(0,"")
exemples = KeyboardStrategy.read("./monfichier.exp")
train,labels = build_apprentissage("./monfichier.exp",gen_features)
tree = apprendre_arbre(train,labels)
print(affiche_arbre(tree))
if __name__=="__main__":
prefix = "./test"
if len(sys.argv)>1:
prefix = sys.argv[1]
## constitution de la base d'entrainement et des labels
train,labels = build_apprentissage(prefix+".exp",gen_features)
## apprentissage de l'arbre
#gen_features.names = ["ball_dist","distance ball my goal","distance ball his goal","hisgoal_dist","mygoal_dist","dist adv le plus proche","nb adv autour"]
def build_apprentissage(fn,generator):
ex_raw = KeyboardStrategy.read(fn)
exemples = []
labels = []
for x in ex_raw:
exemples.append(generator(x[1],x[0][0],x[0][1]))
labels.append(x[0][2])
return exemples,labels
def apprendre_arbre(train,labels,depth=5,min_samples_leaf=2,min_samples_split=2):
tree= DecisionTreeClassifier(max_depth=depth,min_samples_leaf=min_samples_leaf,min_samples_split=min_samples_split)
tree.fit(train,labels)
return tree
## Match d'entrainement et apprentissage de l'arbre
if True:
#match = SoccerMatch(team_noob,team_bad,1000)
#show(match)
## Sauvegarde des exemples, mettre False a True si concatenation des fichiers
#strat.write("test.tree",True)
## Lecture du fichier cree
exemples = KeyboardStrategy.read("./training.exp")
## constitution de la base d'entrainement et des labels
train,labels = build_apprentissage("./training.exp",gen_features)
## apprentissage de l'arbre
tree = apprendre_arbre(train,labels)
## sauvegarde de l'arbre
cPickle.dump(tree,file("tree.pkl","w"))
sepr = " " * long
def aux(node, sep):
if tree.tree_.children_left[node] < 0:
ls = "(%s)" % (", ".join(
"%s: %d" % (tree.classes_[i], int(x))
for i, x in enumerate(tree.tree_.value[node].flat)))
return sep + sep1 + "%s\n" % (ls, )
return (sep+sep1+"X%d<=%0.2f\n"+"%s"+sep+sep1+"X%d>%0.2f\n"+"%s" )% \
(tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_left[node],sep+sepl),
tree.tree_.feature[node],tree.tree_.threshold[node],aux(tree.tree_.children_right[node],sep+sepr))
return aux(0, "")
exemples = KeyboardStrategy.read("./monfichier.exp")
train, labels = build_apprentissage("./monfichier.exp", gen_features)
tree = apprendre_arbre(train, labels)
print(affiche_arbre(tree))
if __name__ == "__main__":
prefix = "./test"
if len(sys.argv) > 1:
prefix = sys.argv[1]
## constitution de la base d'entrainement et des labels
train, labels = build_apprentissage(prefix + ".exp", gen_features)
## apprentissage de l'arbre
tree = apprendre_arbre(train, labels)
## sauvegarde de l'arbre
cPickle.dump(tree, file(prefix + ".pkl", "w"))
## exporter l'arbre en .dot
